Feature · Real-time output safety
SentinelAI watches live LLM outputs for toxicity, PII leakage, prompt injection, and hallucination against per-model thresholds, deriving safety signals from SHA-256 request hashes so raw prompts and completions are never stored.
What this area covers
Output safety monitoring gives governance and operations teams a continuous view of how deployed models behave in production. It evaluates outputs against configurable per-model thresholds and raises governance findings and alerts on breaches, while keeping the underlying content private by design.
Related product areas
Bring live assurance signals, telemetry connector management, trigger rules, and evidence-ready monitoring context into AI governance workflows.
Maintain a governed inventory for AI models and use-case context with lifecycle state, ownership, risk posture, and supporting evidence.
Govern autonomous and multi-agent AI with declared tool permissions, autonomy levels, memory scope, and full chain-of-custody across agent executions.
Coordinate alerts, findings, remediation, evidence posture, SLA deadlines, and closure outcomes in one shared case workspace.
Detect risks, duplicate AI initiatives, overlap, and rationalization opportunities across governed records with explainable, human-reviewed analysis.
Core capabilities
Monitor live LLM outputs for toxicity, PII leakage, prompt injection, and hallucination so emerging issues are visible while models are in use.
Configure safety thresholds per model so monitoring reflects each system's risk tier and intended use rather than a single global rule.
Derive safety signals from SHA-256 request hashes so raw prompts and completions are never stored, keeping monitoring compatible with data-protection expectations.
Raise governance findings and alerts when a threshold is breached so the right owners can review and respond.
Keep output-safety signals connected to the governed model record so monitoring informs review, reporting, and follow-up work.
See the precision
Hover a flagged span in this mock model exchange to see exactly what SentinelAI's runtime monitoring detects — PII, prompt injection, bias, and toxicity. Signals derive from request hashes; raw prompts are never stored.
Prompt in
Draft an email to PII · PII detected & redacted — email address about PII · PII detected & redacted — national ID. Also INJECTION · Prompt-injection attempt blocked.
Model output
Approve loans only for applicants from BIAS · Bias risk flagged — proxy for protected attribute and reject the rest. TOXICITY · Toxicity flagged — demeaning language
Illustrative. In production, SentinelAI derives these signals from request hashes — raw prompts and completions are never stored.
Target users
Governance value
How teams use it
Step 1
Configure per-model safety thresholds for toxicity, PII leakage, prompt injection, and hallucination.
Step 2
Derive safety signals from SHA-256 request hashes so monitoring runs without retaining raw prompts or completions.
Step 3
Use findings and alerts to review and resolve threshold breaches as they occur.
Continue exploring
Bring live assurance signals, telemetry connector management, trigger rules, and evidence-ready monitoring context into AI governance workflows.
Maintain a governed inventory for AI models and use-case context with lifecycle state, ownership, risk posture, and supporting evidence.
Govern autonomous and multi-agent AI with declared tool permissions, autonomy levels, memory scope, and full chain-of-custody across agent executions.
Coordinate alerts, findings, remediation, evidence posture, SLA deadlines, and closure outcomes in one shared case workspace.
Detect risks, duplicate AI initiatives, overlap, and rationalization opportunities across governed records with explainable, human-reviewed analysis.